Liver cancer is mainly caused by hepatitis B and C virus infection. In recent years, the prevalence of hepatitis B and C has been greatly reduced. With poor lifestyle and eating habits, the prevalence of fatty liver disease has increased. Fatty liver disease perhaps gradually replaces viral hepatitis as the leading cause of liver cancer. Ultrasound images are usually the primary checkpoint for the clinical examination of the fatty liver. This study applied a deep learning image segmentation model and image texture feature analysis. First, texture features were extracted from ultrasound images, and then model training was performed on texture features to achieve the clinical objective diagnosis. The US images used in this study were collected from the public medical center US machine. Ultrasound images and FibroScan of liver fibrosis scanner were collected from 235 patients. According to the classification and diagnosis of the severity of fatty liver, this study is divided into two parts. First, the ultrasound image data of patients is applied to image cutting model training and texture feature extraction. Second, the value of the texture feature is compared with the results of liver tissue pathology CAP corresponding to the training and verification of the fatty liver severity classification model. The experimental results show that the proposed model can predict fatty liver disease on a specific instrument and can achieve an area under the curve above 0.8.